Modular Imaging Apps for Automated Microbial Sample Assessment
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Solution Overview
Problem
Existing imaging technologies for culture plates lack automation in interpreting microbial growth, species identification, and susceptibility testing, requiring significant time and resources for development due to diversity in specimen types and organism taxa.
Innovation Solution
A system utilizing modular imaging applications (Apps) that leverage a data lake of historical specimen images, best practices, and artificial intelligence to automate image analysis, enabling rapid development and deployment of Apps for specific media and taxa, with modules for growth detection, identification, and susceptibility testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image processing logic is developed for diagnostic indications, then productivity and automation extent are improved, but device complexity and development time increase due to diversity of specimen types and organism taxa
Solution Approach 1:
The system segments the complex task of automated image processing into distinct modular components: image acquisition module, image analysis module, and diagnostic interpretation module. Each module handles specific aspects of the workflow, making the overall system more manageable and easier to develop despite the diversity of specimen types and organisms.
Solution Approach 2:
The system employs universal algorithms and data structures that can process multiple specimen types and organism taxa through a single integrated platform. The image analysis engine is designed to handle diverse biological samples using common processing pipelines, reducing the need for separate specialized systems for each specimen type.
2Measurement precision
If comprehensive historical data is used for training apps, then measurement precision and reliability are improved, but loss of time and computational resources increase during data processing
Solution Approach 1:
The system performs preliminary processing of historical images during data ingestion, pre-computing features and organizing data into optimized structures before training is needed. This advance preparation reduces the computational burden during actual app development and deployment, maintaining high accuracy while minimizing processing time delays.
Solution Approach 2:
The system selectively processes and stores only the most relevant features and data elements for each specimen type and organism category. Rather than uniformly processing all historical data with the same computational intensity, it applies localized processing strategies tailored to specific data characteristics, improving efficiency without sacrificing identification accuracy.
Data Source
AI summary
A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.